Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.
Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis.
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DOI：
10.1097/rct.0000000000000394
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发表时间：
2016-07
影响因子：
1.3
通讯作者：
Meyers BF
中科院分区：
文献类型：
--
作者：
Gierada DS;Politte DG;Zheng J;Schechtman KB;Whiting BR;Smith KE;Crabtree T;Kreisel D;Krupnick AS;Patterson GA;Puri V;Meyers BF
比较2D和3D定量CT方法将肺结节分类为肺癌、转移瘤或良性的性能。使用半自动化软件和计算机分析，我们分析了94例患者96个实性结节的50多个定量CT特征，从单个切片的2D和从整个结节体积的3D。采用多变量逻辑回归对结节类型进行分类。使用留一法交叉验证，通过受试者工作特征曲线下面积（AUC）评估模型性能。区分53例原发性肺癌与18例良性结节和25例转移瘤的AUC范围为0.79 - 0.83，2D和3D分析无显著差异（p=0.29-0.78）。仅通过3D分析（AUC=0.84）区分转移瘤与良性结节的模型具有统计学显著性。3D CT方法不能提高肺癌的识别率，但可能有助于区分良性结节和转移瘤。
To compare the performance of 2D and 3D quantitative CT methods for classifying lung nodules as lung cancer, metastases, or benign. Using semiautomated software and computerized analysis, we analyzed more than 50 quantitative CT features of 96 solid nodules in 94 patients, in 2D from a single slice and in 3D from the entire nodule volume. Multivariable logistic regression was used to classify nodule types. Model performance was assessed by the area under the receiver-operating characteristic curve (AUC) using leave-one-out cross-validation. The AUC for distinguishing 53 primary lung cancers from 18 benign nodules and 25 metastases ranged from 0.79 to 0.83 and was not significantly different for 2D and 3D analyses (p=0.29–0.78). Models distinguishing metastases from benign nodules were statistically significant only by 3D analysis (AUC=0.84). 3D CT methods did not improve discrimination of lung cancer, but may help distinguish benign nodules from metastases.